Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations396030
Missing cells81590
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory339.4 MiB
Average record size in memory898.7 B

Variable types

Numeric12
Categorical10
Text3
DateTime2

Alerts

dti is highly overall correlated with emp_lengthHigh correlation
emp_length is highly overall correlated with dtiHigh correlation
grade is highly overall correlated with int_rate and 1 other fieldsHigh correlation
installment is highly overall correlated with loan_amntHigh correlation
int_rate is highly overall correlated with grade and 1 other fieldsHigh correlation
loan_amnt is highly overall correlated with installmentHigh correlation
open_acc is highly overall correlated with total_accHigh correlation
pub_rec is highly overall correlated with pub_rec_bankruptciesHigh correlation
pub_rec_bankruptcies is highly overall correlated with pub_recHigh correlation
sub_grade is highly overall correlated with grade and 1 other fieldsHigh correlation
total_acc is highly overall correlated with open_accHigh correlation
application_type is highly imbalanced (98.7%)Imbalance
emp_title has 22927 (5.8%) missing valuesMissing
emp_length has 18301 (4.6%) missing valuesMissing
mort_acc has 37795 (9.5%) missing valuesMissing
annual_inc is highly skewed (γ1 = 41.04272475)Skewed
dti is highly skewed (γ1 = 431.0512254)Skewed
pub_rec has 338272 (85.4%) zerosZeros
mort_acc has 139777 (35.3%) zerosZeros
pub_rec_bankruptcies has 350380 (88.5%) zerosZeros

Reproduction

Analysis started2024-10-06 21:24:54.308203
Analysis finished2024-10-06 21:25:25.795716
Duration31.49 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

loan_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct1397
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14113.888
Minimum500
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:25.862157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile3250
Q18000
median12000
Q320000
95-th percentile30975
Maximum40000
Range39500
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation8357.4413
Coefficient of variation (CV)0.59214309
Kurtosis-0.062597535
Mean14113.888
Median Absolute Deviation (MAD)5500
Skewness0.77728547
Sum5.5895231 × 109
Variance69846826
MonotonicityNot monotonic
2024-10-07T02:55:25.925466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27668
 
7.0%
12000 21366
 
5.4%
15000 19903
 
5.0%
20000 18969
 
4.8%
35000 14576
 
3.7%
8000 13539
 
3.4%
6000 12734
 
3.2%
5000 12443
 
3.1%
16000 10129
 
2.6%
18000 9195
 
2.3%
Other values (1387) 235508
59.5%
ValueCountFrequency (%)
500 4
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 1
 
< 0.1%
950 1
 
< 0.1%
1000 1448
0.4%
1025 4
 
< 0.1%
1050 10
 
< 0.1%
ValueCountFrequency (%)
40000 180
< 0.1%
39700 1
 
< 0.1%
39600 1
 
< 0.1%
39500 1
 
< 0.1%
39475 1
 
< 0.1%
39200 1
 
< 0.1%
38825 1
 
< 0.1%
38750 1
 
< 0.1%
38475 1
 
< 0.1%
38300 1
 
< 0.1%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.3 MiB
36 months
302005 
60 months
94025 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3960300
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 36 months
3rd row 36 months
4th row 36 months
5th row 60 months

Common Values

ValueCountFrequency (%)
36 months 302005
76.3%
60 months 94025
 
23.7%

Length

2024-10-07T02:55:25.980953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T02:55:26.066703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
months 396030
50.0%
36 302005
38.1%
60 94025
 
11.9%

Most occurring characters

ValueCountFrequency (%)
792060
20.0%
6 396030
10.0%
t 396030
10.0%
m 396030
10.0%
o 396030
10.0%
n 396030
10.0%
s 396030
10.0%
h 396030
10.0%
3 302005
 
7.6%
0 94025
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3960300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
792060
20.0%
6 396030
10.0%
t 396030
10.0%
m 396030
10.0%
o 396030
10.0%
n 396030
10.0%
s 396030
10.0%
h 396030
10.0%
3 302005
 
7.6%
0 94025
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3960300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
792060
20.0%
6 396030
10.0%
t 396030
10.0%
m 396030
10.0%
o 396030
10.0%
n 396030
10.0%
s 396030
10.0%
h 396030
10.0%
3 302005
 
7.6%
0 94025
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3960300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
792060
20.0%
6 396030
10.0%
t 396030
10.0%
m 396030
10.0%
o 396030
10.0%
n 396030
10.0%
s 396030
10.0%
h 396030
10.0%
3 302005
 
7.6%
0 94025
 
2.4%

int_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct566
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.6394
Minimum5.32
Maximum30.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:26.119366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.32
5-th percentile6.89
Q110.49
median13.33
Q316.49
95-th percentile21.97
Maximum30.99
Range25.67
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4721574
Coefficient of variation (CV)0.3278852
Kurtosis-0.14394654
Mean13.6394
Median Absolute Deviation (MAD)3.08
Skewness0.42066947
Sum5401611.6
Variance20.000192
MonotonicityNot monotonic
2024-10-07T02:55:26.178762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.99 12411
 
3.1%
12.99 9632
 
2.4%
15.61 9350
 
2.4%
11.99 8582
 
2.2%
8.9 8019
 
2.0%
12.12 7358
 
1.9%
7.9 7332
 
1.9%
16.29 6632
 
1.7%
13.11 6580
 
1.7%
6.03 6291
 
1.6%
Other values (556) 313843
79.2%
ValueCountFrequency (%)
5.32 2440
 
0.6%
5.42 465
 
0.1%
5.79 333
 
0.1%
5.93 431
 
0.1%
5.99 278
 
0.1%
6 70
 
< 0.1%
6.03 6291
1.6%
6.17 220
 
0.1%
6.24 1184
 
0.3%
6.39 656
 
0.2%
ValueCountFrequency (%)
30.99 13
< 0.1%
30.94 3
 
< 0.1%
30.89 3
 
< 0.1%
30.84 1
 
< 0.1%
30.79 9
< 0.1%
30.74 4
 
< 0.1%
30.49 5
 
< 0.1%
29.99 7
< 0.1%
29.96 8
< 0.1%
29.67 15
< 0.1%

installment
Real number (ℝ)

HIGH CORRELATION 

Distinct55706
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean431.8497
Minimum16.08
Maximum1533.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:26.238302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum16.08
5-th percentile109.51
Q1250.33
median375.43
Q3567.3
95-th percentile925.6
Maximum1533.81
Range1517.73
Interquartile range (IQR)316.97

Descriptive statistics

Standard deviation250.72779
Coefficient of variation (CV)0.5805904
Kurtosis0.78381992
Mean431.8497
Median Absolute Deviation (MAD)150.5
Skewness0.98359816
Sum1.7102544 × 108
Variance62864.424
MonotonicityNot monotonic
2024-10-07T02:55:26.450526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
327.34 968
 
0.2%
332.1 791
 
0.2%
491.01 736
 
0.2%
336.9 686
 
0.2%
392.81 683
 
0.2%
332.72 641
 
0.2%
337.47 624
 
0.2%
317.54 574
 
0.1%
654.68 556
 
0.1%
261.88 527
 
0.1%
Other values (55696) 389244
98.3%
ValueCountFrequency (%)
16.08 1
< 0.1%
16.25 1
< 0.1%
16.31 1
< 0.1%
16.47 1
< 0.1%
19.87 1
< 0.1%
20.22 1
< 0.1%
21.25 1
< 0.1%
21.62 1
< 0.1%
21.99 1
< 0.1%
22.24 1
< 0.1%
ValueCountFrequency (%)
1533.81 1
< 0.1%
1527 1
< 0.1%
1503.85 1
< 0.1%
1479.49 1
< 0.1%
1464.42 1
< 0.1%
1458.25 1
< 0.1%
1451.14 2
< 0.1%
1451.12 2
< 0.1%
1445.9 1
< 0.1%
1443.76 1
< 0.1%

grade
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.9 MiB
B
116018 
C
105987 
A
64187 
D
63524 
E
31488 
Other values (2)
14826 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters396030
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowC

Common Values

ValueCountFrequency (%)
B 116018
29.3%
C 105987
26.8%
A 64187
16.2%
D 63524
16.0%
E 31488
 
8.0%
F 11772
 
3.0%
G 3054
 
0.8%

Length

2024-10-07T02:55:26.509130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T02:55:26.557950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 116018
29.3%
c 105987
26.8%
a 64187
16.2%
d 63524
16.0%
e 31488
 
8.0%
f 11772
 
3.0%
g 3054
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B 116018
29.3%
C 105987
26.8%
A 64187
16.2%
D 63524
16.0%
E 31488
 
8.0%
F 11772
 
3.0%
G 3054
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 396030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 116018
29.3%
C 105987
26.8%
A 64187
16.2%
D 63524
16.0%
E 31488
 
8.0%
F 11772
 
3.0%
G 3054
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 396030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 116018
29.3%
C 105987
26.8%
A 64187
16.2%
D 63524
16.0%
E 31488
 
8.0%
F 11772
 
3.0%
G 3054
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 396030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 116018
29.3%
C 105987
26.8%
A 64187
16.2%
D 63524
16.0%
E 31488
 
8.0%
F 11772
 
3.0%
G 3054
 
0.8%

sub_grade
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.3 MiB
B3
 
26655
B4
 
25601
C1
 
23662
C2
 
22580
B2
 
22495
Other values (30)
275037 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters792060
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB4
2nd rowB5
3rd rowB3
4th rowA2
5th rowC5

Common Values

ValueCountFrequency (%)
B3 26655
 
6.7%
B4 25601
 
6.5%
C1 23662
 
6.0%
C2 22580
 
5.7%
B2 22495
 
5.7%
B5 22085
 
5.6%
C3 21221
 
5.4%
C4 20280
 
5.1%
B1 19182
 
4.8%
A5 18526
 
4.7%
Other values (25) 173743
43.9%

Length

2024-10-07T02:55:26.617330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b3 26655
 
6.7%
b4 25601
 
6.5%
c1 23662
 
6.0%
c2 22580
 
5.7%
b2 22495
 
5.7%
b5 22085
 
5.6%
c3 21221
 
5.4%
c4 20280
 
5.1%
b1 19182
 
4.8%
a5 18526
 
4.7%
Other values (25) 173743
43.9%

Most occurring characters

ValueCountFrequency (%)
B 116018
14.6%
C 105987
13.4%
1 81077
10.2%
4 80849
10.2%
3 79720
10.1%
2 79544
10.0%
5 74840
9.4%
A 64187
8.1%
D 63524
8.0%
E 31488
 
4.0%
Other values (2) 14826
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 792060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 116018
14.6%
C 105987
13.4%
1 81077
10.2%
4 80849
10.2%
3 79720
10.1%
2 79544
10.0%
5 74840
9.4%
A 64187
8.1%
D 63524
8.0%
E 31488
 
4.0%
Other values (2) 14826
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 792060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 116018
14.6%
C 105987
13.4%
1 81077
10.2%
4 80849
10.2%
3 79720
10.1%
2 79544
10.0%
5 74840
9.4%
A 64187
8.1%
D 63524
8.0%
E 31488
 
4.0%
Other values (2) 14826
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 792060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 116018
14.6%
C 105987
13.4%
1 81077
10.2%
4 80849
10.2%
3 79720
10.1%
2 79544
10.0%
5 74840
9.4%
A 64187
8.1%
D 63524
8.0%
E 31488
 
4.0%
Other values (2) 14826
 
1.9%

emp_title
Text

MISSING 

Distinct173105
Distinct (%)46.4%
Missing22927
Missing (%)5.8%
Memory size24.0 MiB
2024-10-07T02:55:26.771566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length78
Median length56
Mean length16.586736
Min length1

Characters and Unicode

Total characters6188561
Distinct characters125
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145247 ?
Unique (%)38.9%

Sample

1st rowMarketing
2nd rowCredit analyst
3rd rowStatistician
4th rowClient Advocate
5th rowDestiny Management Inc.
ValueCountFrequency (%)
manager 39270
 
4.7%
of 15802
 
1.9%
inc 10469
 
1.2%
director 9837
 
1.2%
sales 9635
 
1.1%
assistant 9259
 
1.1%
analyst 7652
 
0.9%
specialist 7627
 
0.9%
supervisor 7501
 
0.9%
engineer 7462
 
0.9%
Other values (55359) 717784
85.2%
2024-10-07T02:55:27.036636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 606206
 
9.8%
487836
 
7.9%
r 470449
 
7.6%
a 455384
 
7.4%
i 406094
 
6.6%
n 405205
 
6.5%
t 373457
 
6.0%
o 330975
 
5.3%
s 293945
 
4.7%
c 244175
 
3.9%
Other values (115) 2114835
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6188561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 606206
 
9.8%
487836
 
7.9%
r 470449
 
7.6%
a 455384
 
7.4%
i 406094
 
6.6%
n 405205
 
6.5%
t 373457
 
6.0%
o 330975
 
5.3%
s 293945
 
4.7%
c 244175
 
3.9%
Other values (115) 2114835
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6188561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 606206
 
9.8%
487836
 
7.9%
r 470449
 
7.6%
a 455384
 
7.4%
i 406094
 
6.6%
n 405205
 
6.5%
t 373457
 
6.0%
o 330975
 
5.3%
s 293945
 
4.7%
c 244175
 
3.9%
Other values (115) 2114835
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6188561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 606206
 
9.8%
487836
 
7.9%
r 470449
 
7.6%
a 455384
 
7.4%
i 406094
 
6.6%
n 405205
 
6.5%
t 373457
 
6.0%
o 330975
 
5.3%
s 293945
 
4.7%
c 244175
 
3.9%
Other values (115) 2114835
34.2%

emp_length
Categorical

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)< 0.1%
Missing18301
Missing (%)4.6%
Memory size21.4 MiB
10+ years
126041 
2 years
35827 
< 1 year
31725 
3 years
31665 
5 years
26495 
Other values (6)
125976 

Length

Max length9
Median length7
Mean length7.6828308
Min length6

Characters and Unicode

Total characters2902028
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row4 years
3rd row< 1 year
4th row6 years
5th row9 years

Common Values

ValueCountFrequency (%)
10+ years 126041
31.8%
2 years 35827
 
9.0%
< 1 year 31725
 
8.0%
3 years 31665
 
8.0%
5 years 26495
 
6.7%
1 year 25882
 
6.5%
4 years 23952
 
6.0%
6 years 20841
 
5.3%
7 years 20819
 
5.3%
8 years 19168
 
4.8%
(Missing) 18301
 
4.6%

Length

2024-10-07T02:55:27.103047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 320122
40.7%
10 126041
 
16.0%
1 57607
 
7.3%
year 57607
 
7.3%
2 35827
 
4.6%
31725
 
4.0%
3 31665
 
4.0%
5 26495
 
3.4%
4 23952
 
3.0%
6 20841
 
2.6%
Other values (3) 55301
 
7.0%

Most occurring characters

ValueCountFrequency (%)
409454
14.1%
y 377729
13.0%
r 377729
13.0%
a 377729
13.0%
e 377729
13.0%
s 320122
11.0%
1 183648
6.3%
0 126041
 
4.3%
+ 126041
 
4.3%
2 35827
 
1.2%
Other values (8) 189979
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2902028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
409454
14.1%
y 377729
13.0%
r 377729
13.0%
a 377729
13.0%
e 377729
13.0%
s 320122
11.0%
1 183648
6.3%
0 126041
 
4.3%
+ 126041
 
4.3%
2 35827
 
1.2%
Other values (8) 189979
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2902028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
409454
14.1%
y 377729
13.0%
r 377729
13.0%
a 377729
13.0%
e 377729
13.0%
s 320122
11.0%
1 183648
6.3%
0 126041
 
4.3%
+ 126041
 
4.3%
2 35827
 
1.2%
Other values (8) 189979
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2902028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
409454
14.1%
y 377729
13.0%
r 377729
13.0%
a 377729
13.0%
e 377729
13.0%
s 320122
11.0%
1 183648
6.3%
0 126041
 
4.3%
+ 126041
 
4.3%
2 35827
 
1.2%
Other values (8) 189979
6.5%

home_ownership
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.7 MiB
MORTGAGE
198348 
RENT
159790 
OWN
37746 
OTHER
 
112
NONE
 
31

Length

Max length8
Median length8
Mean length5.9083277
Min length3

Characters and Unicode

Total characters2339875
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowMORTGAGE
3rd rowRENT
4th rowRENT
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
MORTGAGE 198348
50.1%
RENT 159790
40.3%
OWN 37746
 
9.5%
OTHER 112
 
< 0.1%
NONE 31
 
< 0.1%
ANY 3
 
< 0.1%

Length

2024-10-07T02:55:27.156881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T02:55:27.206277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 198348
50.1%
rent 159790
40.3%
own 37746
 
9.5%
other 112
 
< 0.1%
none 31
 
< 0.1%
any 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 396696
17.0%
E 358281
15.3%
R 358250
15.3%
T 358250
15.3%
O 236237
10.1%
A 198351
8.5%
M 198348
8.5%
N 197601
8.4%
W 37746
 
1.6%
H 112
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2339875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 396696
17.0%
E 358281
15.3%
R 358250
15.3%
T 358250
15.3%
O 236237
10.1%
A 198351
8.5%
M 198348
8.5%
N 197601
8.4%
W 37746
 
1.6%
H 112
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2339875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 396696
17.0%
E 358281
15.3%
R 358250
15.3%
T 358250
15.3%
O 236237
10.1%
A 198351
8.5%
M 198348
8.5%
N 197601
8.4%
W 37746
 
1.6%
H 112
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2339875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 396696
17.0%
E 358281
15.3%
R 358250
15.3%
T 358250
15.3%
O 236237
10.1%
A 198351
8.5%
M 198348
8.5%
N 197601
8.4%
W 37746
 
1.6%
H 112
 
< 0.1%

annual_inc
Real number (ℝ)

SKEWED 

Distinct27197
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74203.176
Minimum0
Maximum8706582
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:27.270485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28000
Q145000
median64000
Q390000
95-th percentile150000
Maximum8706582
Range8706582
Interquartile range (IQR)45000

Descriptive statistics

Standard deviation61637.621
Coefficient of variation (CV)0.83066015
Kurtosis4238.5506
Mean74203.176
Median Absolute Deviation (MAD)21000
Skewness41.042725
Sum2.9386684 × 1010
Variance3.7991963 × 109
MonotonicityNot monotonic
2024-10-07T02:55:27.331146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 15313
 
3.9%
50000 13303
 
3.4%
65000 11333
 
2.9%
70000 10674
 
2.7%
40000 10629
 
2.7%
45000 10114
 
2.6%
80000 9971
 
2.5%
75000 9850
 
2.5%
55000 9195
 
2.3%
90000 7573
 
1.9%
Other values (27187) 288075
72.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
600 1
 
< 0.1%
2500 1
 
< 0.1%
4000 2
 
< 0.1%
4080 1
 
< 0.1%
4200 1
 
< 0.1%
4524 1
 
< 0.1%
4800 6
< 0.1%
4888 1
 
< 0.1%
5000 3
< 0.1%
ValueCountFrequency (%)
8706582 1
< 0.1%
7600000 1
< 0.1%
7446395 1
< 0.1%
7141778 1
< 0.1%
7000000 1
< 0.1%
6500000 1
< 0.1%
6100000 1
< 0.1%
6000000 2
< 0.1%
5000000 1
< 0.1%
4900000 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.9 MiB
Verified
139563 
Source Verified
131385 
Not Verified
125082 

Length

Max length15
Median length12
Mean length11.585645
Min length8

Characters and Unicode

Total characters4588263
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowNot Verified
3rd rowSource Verified
4th rowNot Verified
5th rowVerified

Common Values

ValueCountFrequency (%)
Verified 139563
35.2%
Source Verified 131385
33.2%
Not Verified 125082
31.6%

Length

2024-10-07T02:55:27.392493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T02:55:27.441938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
verified 396030
60.7%
source 131385
 
20.1%
not 125082
 
19.2%

Most occurring characters

ValueCountFrequency (%)
e 923445
20.1%
i 792060
17.3%
r 527415
11.5%
V 396030
8.6%
f 396030
8.6%
d 396030
8.6%
o 256467
 
5.6%
256467
 
5.6%
S 131385
 
2.9%
u 131385
 
2.9%
Other values (3) 381549
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4588263
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 923445
20.1%
i 792060
17.3%
r 527415
11.5%
V 396030
8.6%
f 396030
8.6%
d 396030
8.6%
o 256467
 
5.6%
256467
 
5.6%
S 131385
 
2.9%
u 131385
 
2.9%
Other values (3) 381549
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4588263
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 923445
20.1%
i 792060
17.3%
r 527415
11.5%
V 396030
8.6%
f 396030
8.6%
d 396030
8.6%
o 256467
 
5.6%
256467
 
5.6%
S 131385
 
2.9%
u 131385
 
2.9%
Other values (3) 381549
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4588263
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 923445
20.1%
i 792060
17.3%
r 527415
11.5%
V 396030
8.6%
f 396030
8.6%
d 396030
8.6%
o 256467
 
5.6%
256467
 
5.6%
S 131385
 
2.9%
u 131385
 
2.9%
Other values (3) 381549
8.3%
Distinct115
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2007-06-01 00:00:00
Maximum2016-12-01 00:00:00
2024-10-07T02:55:27.514688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:27.574900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
Fully Paid
318357 
Charged Off
77673 

Length

Max length11
Median length10
Mean length10.196129
Min length10

Characters and Unicode

Total characters4037973
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowFully Paid
3rd rowFully Paid
4th rowFully Paid
5th rowCharged Off

Common Values

ValueCountFrequency (%)
Fully Paid 318357
80.4%
Charged Off 77673
 
19.6%

Length

2024-10-07T02:55:27.631823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T02:55:27.678401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
fully 318357
40.2%
paid 318357
40.2%
charged 77673
 
9.8%
off 77673
 
9.8%

Most occurring characters

ValueCountFrequency (%)
l 636714
15.8%
396030
9.8%
a 396030
9.8%
d 396030
9.8%
y 318357
7.9%
u 318357
7.9%
P 318357
7.9%
F 318357
7.9%
i 318357
7.9%
f 155346
 
3.8%
Other values (6) 466038
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4037973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 636714
15.8%
396030
9.8%
a 396030
9.8%
d 396030
9.8%
y 318357
7.9%
u 318357
7.9%
P 318357
7.9%
F 318357
7.9%
i 318357
7.9%
f 155346
 
3.8%
Other values (6) 466038
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4037973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 636714
15.8%
396030
9.8%
a 396030
9.8%
d 396030
9.8%
y 318357
7.9%
u 318357
7.9%
P 318357
7.9%
F 318357
7.9%
i 318357
7.9%
f 155346
 
3.8%
Other values (6) 466038
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4037973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 636714
15.8%
396030
9.8%
a 396030
9.8%
d 396030
9.8%
y 318357
7.9%
u 318357
7.9%
P 318357
7.9%
F 318357
7.9%
i 318357
7.9%
f 155346
 
3.8%
Other values (6) 466038
11.5%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.2 MiB
debt_consolidation
234507 
credit_card
83019 
home_improvement
24030 
other
 
21185
major_purchase
 
8790
Other values (9)
24499 

Length

Max length18
Median length18
Mean length14.997846
Min length3

Characters and Unicode

Total characters5939597
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowvacation
2nd rowdebt_consolidation
3rd rowcredit_card
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
debt_consolidation 234507
59.2%
credit_card 83019
 
21.0%
home_improvement 24030
 
6.1%
other 21185
 
5.3%
major_purchase 8790
 
2.2%
small_business 5701
 
1.4%
car 4697
 
1.2%
medical 4196
 
1.1%
moving 2854
 
0.7%
vacation 2452
 
0.6%
Other values (4) 4599
 
1.2%

Length

2024-10-07T02:55:27.731178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 234507
59.2%
credit_card 83019
 
21.0%
home_improvement 24030
 
6.1%
other 21185
 
5.3%
major_purchase 8790
 
2.2%
small_business 5701
 
1.4%
car 4697
 
1.2%
medical 4196
 
1.1%
moving 2854
 
0.7%
vacation 2452
 
0.6%
Other values (4) 4599
 
1.2%

Most occurring characters

ValueCountFrequency (%)
o 789320
13.3%
d 643129
10.8%
t 599957
10.1%
i 593335
10.0%
n 506778
8.5%
e 435403
7.3%
c 420937
7.1%
_ 356376
 
6.0%
a 355447
 
6.0%
s 268302
 
4.5%
Other values (12) 970613
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5939597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 789320
13.3%
d 643129
10.8%
t 599957
10.1%
i 593335
10.0%
n 506778
8.5%
e 435403
7.3%
c 420937
7.1%
_ 356376
 
6.0%
a 355447
 
6.0%
s 268302
 
4.5%
Other values (12) 970613
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5939597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 789320
13.3%
d 643129
10.8%
t 599957
10.1%
i 593335
10.0%
n 506778
8.5%
e 435403
7.3%
c 420937
7.1%
_ 356376
 
6.0%
a 355447
 
6.0%
s 268302
 
4.5%
Other values (12) 970613
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5939597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 789320
13.3%
d 643129
10.8%
t 599957
10.1%
i 593335
10.0%
n 506778
8.5%
e 435403
7.3%
c 420937
7.1%
_ 356376
 
6.0%
a 355447
 
6.0%
s 268302
 
4.5%
Other values (12) 970613
16.3%

title
Text

Distinct48816
Distinct (%)12.4%
Missing1756
Missing (%)0.4%
Memory size25.0 MiB
2024-10-07T02:55:27.895765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length80
Median length79
Mean length17.241127
Min length2

Characters and Unicode

Total characters6797728
Distinct characters101
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41797 ?
Unique (%)10.6%

Sample

1st rowVacation
2nd rowDebt consolidation
3rd rowCredit card refinancing
4th rowCredit card refinancing
5th rowCredit Card Refinance
ValueCountFrequency (%)
consolidation 191014
21.7%
debt 190821
21.6%
credit 74290
 
8.4%
card 68254
 
7.7%
refinancing 52262
 
5.9%
loan 28112
 
3.2%
home 22625
 
2.6%
improvement 18786
 
2.1%
other 13252
 
1.5%
payoff 6685
 
0.8%
Other values (14633) 216173
24.5%
2024-10-07T02:55:28.153212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 735790
10.8%
n 682850
 
10.0%
i 655694
 
9.6%
t 545268
 
8.0%
e 521003
 
7.7%
494561
 
7.3%
a 445747
 
6.6%
d 386101
 
5.7%
c 322828
 
4.7%
r 295630
 
4.3%
Other values (91) 1712256
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6797728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 735790
10.8%
n 682850
 
10.0%
i 655694
 
9.6%
t 545268
 
8.0%
e 521003
 
7.7%
494561
 
7.3%
a 445747
 
6.6%
d 386101
 
5.7%
c 322828
 
4.7%
r 295630
 
4.3%
Other values (91) 1712256
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6797728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 735790
10.8%
n 682850
 
10.0%
i 655694
 
9.6%
t 545268
 
8.0%
e 521003
 
7.7%
494561
 
7.3%
a 445747
 
6.6%
d 386101
 
5.7%
c 322828
 
4.7%
r 295630
 
4.3%
Other values (91) 1712256
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6797728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 735790
10.8%
n 682850
 
10.0%
i 655694
 
9.6%
t 545268
 
8.0%
e 521003
 
7.7%
494561
 
7.3%
a 445747
 
6.6%
d 386101
 
5.7%
c 322828
 
4.7%
r 295630
 
4.3%
Other values (91) 1712256
25.2%

dti
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4262
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.379514
Minimum0
Maximum9999
Zeros313
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:28.221338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.68
Q111.28
median16.91
Q322.98
95-th percentile31.58
Maximum9999
Range9999
Interquartile range (IQR)11.7

Descriptive statistics

Standard deviation18.019092
Coefficient of variation (CV)1.0368007
Kurtosis237923.68
Mean17.379514
Median Absolute Deviation (MAD)5.83
Skewness431.05123
Sum6882808.8
Variance324.68769
MonotonicityNot monotonic
2024-10-07T02:55:28.283654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 313
 
0.1%
14.4 310
 
0.1%
19.2 302
 
0.1%
16.8 301
 
0.1%
18 300
 
0.1%
20.4 296
 
0.1%
12 293
 
0.1%
13.2 291
 
0.1%
21.6 270
 
0.1%
15.6 266
 
0.1%
Other values (4252) 393088
99.3%
ValueCountFrequency (%)
0 313
0.1%
0.01 8
 
< 0.1%
0.02 12
 
< 0.1%
0.03 5
 
< 0.1%
0.04 5
 
< 0.1%
0.05 6
 
< 0.1%
0.06 7
 
< 0.1%
0.07 7
 
< 0.1%
0.08 8
 
< 0.1%
0.09 4
 
< 0.1%
ValueCountFrequency (%)
9999 1
< 0.1%
1622 1
< 0.1%
380.53 1
< 0.1%
189.9 1
< 0.1%
145.65 1
< 0.1%
138.03 1
< 0.1%
120.66 1
< 0.1%
107.55 1
< 0.1%
93.86 1
< 0.1%
92.13 1
< 0.1%
Distinct684
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum1944-01-01 00:00:00
Maximum2013-10-01 00:00:00
2024-10-07T02:55:28.341789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:28.395845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

open_acc
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.311153
Minimum0
Maximum90
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:28.450774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median10
Q314
95-th percentile21
Maximum90
Range90
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1376488
Coefficient of variation (CV)0.45421088
Kurtosis2.9669448
Mean11.311153
Median Absolute Deviation (MAD)3
Skewness1.2130188
Sum4479556
Variance26.395435
MonotonicityNot monotonic
2024-10-07T02:55:28.509193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 36779
 
9.3%
10 35441
 
8.9%
8 35137
 
8.9%
11 32695
 
8.3%
7 31328
 
7.9%
12 29157
 
7.4%
6 25927
 
6.5%
13 24983
 
6.3%
14 21173
 
5.3%
5 18308
 
4.6%
Other values (51) 105102
26.5%
ValueCountFrequency (%)
0 6
 
< 0.1%
1 85
 
< 0.1%
2 1459
 
0.4%
3 4783
 
1.2%
4 10709
 
2.7%
5 18308
4.6%
6 25927
6.5%
7 31328
7.9%
8 35137
8.9%
9 36779
9.3%
ValueCountFrequency (%)
90 1
 
< 0.1%
76 2
 
< 0.1%
58 1
 
< 0.1%
57 1
 
< 0.1%
56 2
 
< 0.1%
55 2
 
< 0.1%
54 3
< 0.1%
53 6
< 0.1%
52 3
< 0.1%
51 4
< 0.1%

pub_rec
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17819105
Minimum0
Maximum86
Zeros338272
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:28.564160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum86
Range86
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5306706
Coefficient of variation (CV)2.9780991
Kurtosis1867.4666
Mean0.17819105
Median Absolute Deviation (MAD)0
Skewness16.576564
Sum70569
Variance0.28161129
MonotonicityNot monotonic
2024-10-07T02:55:28.613747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 338272
85.4%
1 49739
 
12.6%
2 5476
 
1.4%
3 1521
 
0.4%
4 527
 
0.1%
5 237
 
0.1%
6 122
 
< 0.1%
7 56
 
< 0.1%
8 34
 
< 0.1%
9 12
 
< 0.1%
Other values (10) 34
 
< 0.1%
ValueCountFrequency (%)
0 338272
85.4%
1 49739
 
12.6%
2 5476
 
1.4%
3 1521
 
0.4%
4 527
 
0.1%
5 237
 
0.1%
6 122
 
< 0.1%
7 56
 
< 0.1%
8 34
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
86 1
 
< 0.1%
40 1
 
< 0.1%
24 1
 
< 0.1%
19 2
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
13 4
 
< 0.1%
12 4
 
< 0.1%
11 8
< 0.1%
10 11
< 0.1%

revol_bal
Real number (ℝ)

Distinct55622
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15844.54
Minimum0
Maximum1743266
Zeros2128
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:28.672852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1685
Q16025
median11181
Q319620
95-th percentile41066.55
Maximum1743266
Range1743266
Interquartile range (IQR)13595

Descriptive statistics

Standard deviation20591.836
Coefficient of variation (CV)1.2996172
Kurtosis384.22109
Mean15844.54
Median Absolute Deviation (MAD)6112
Skewness11.727515
Sum6.2749131 × 109
Variance4.2402371 × 108
MonotonicityNot monotonic
2024-10-07T02:55:28.891928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2128
 
0.5%
5655 41
 
< 0.1%
6095 38
 
< 0.1%
7792 38
 
< 0.1%
3953 37
 
< 0.1%
5098 36
 
< 0.1%
6077 36
 
< 0.1%
4541 35
 
< 0.1%
5389 35
 
< 0.1%
5235 35
 
< 0.1%
Other values (55612) 393571
99.4%
ValueCountFrequency (%)
0 2128
0.5%
1 30
 
< 0.1%
2 26
 
< 0.1%
3 28
 
< 0.1%
4 20
 
< 0.1%
5 23
 
< 0.1%
6 30
 
< 0.1%
7 21
 
< 0.1%
8 21
 
< 0.1%
9 23
 
< 0.1%
ValueCountFrequency (%)
1743266 1
< 0.1%
1298783 1
< 0.1%
1190046 1
< 0.1%
1030826 1
< 0.1%
1023940 1
< 0.1%
975800 1
< 0.1%
867528 1
< 0.1%
838698 1
< 0.1%
814300 1
< 0.1%
778614 1
< 0.1%

revol_util
Real number (ℝ)

Distinct1226
Distinct (%)0.3%
Missing276
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean53.791749
Minimum0
Maximum892.3
Zeros2213
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:28.951169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.2
Q135.8
median54.8
Q372.9
95-th percentile92
Maximum892.3
Range892.3
Interquartile range (IQR)37.1

Descriptive statistics

Standard deviation24.452193
Coefficient of variation (CV)0.45457145
Kurtosis2.7122782
Mean53.791749
Median Absolute Deviation (MAD)18.5
Skewness-0.07177802
Sum21288300
Variance597.90975
MonotonicityNot monotonic
2024-10-07T02:55:29.007578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2213
 
0.6%
53 752
 
0.2%
60 739
 
0.2%
61 734
 
0.2%
55 730
 
0.2%
54 725
 
0.2%
62 721
 
0.2%
47 720
 
0.2%
57 719
 
0.2%
58 717
 
0.2%
Other values (1216) 386984
97.7%
ValueCountFrequency (%)
0 2213
0.6%
0.01 1
 
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.1 253
 
0.1%
0.16 1
 
< 0.1%
0.2 211
 
0.1%
0.3 187
 
< 0.1%
0.4 189
 
< 0.1%
0.46 1
 
< 0.1%
ValueCountFrequency (%)
892.3 1
< 0.1%
153 1
< 0.1%
152.5 1
< 0.1%
150.7 1
< 0.1%
148 1
< 0.1%
146.1 1
< 0.1%
145.8 1
< 0.1%
140.4 1
< 0.1%
136.7 1
< 0.1%
132.1 1
< 0.1%

total_acc
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.414744
Minimum2
Maximum151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:29.062324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q117
median24
Q332
95-th percentile47
Maximum151
Range149
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.886991
Coefficient of variation (CV)0.46772027
Kurtosis1.20462
Mean25.414744
Median Absolute Deviation (MAD)8
Skewness0.86432764
Sum10065001
Variance141.30055
MonotonicityNot monotonic
2024-10-07T02:55:29.119589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 14280
 
3.6%
22 14260
 
3.6%
20 14228
 
3.6%
23 13923
 
3.5%
24 13878
 
3.5%
19 13876
 
3.5%
18 13710
 
3.5%
17 13495
 
3.4%
25 13225
 
3.3%
26 12799
 
3.2%
Other values (108) 258356
65.2%
ValueCountFrequency (%)
2 18
 
< 0.1%
3 327
 
0.1%
4 1238
 
0.3%
5 2028
 
0.5%
6 2923
 
0.7%
7 4143
1.0%
8 5365
1.4%
9 6362
1.6%
10 7672
1.9%
11 8844
2.2%
ValueCountFrequency (%)
151 1
< 0.1%
150 1
< 0.1%
135 1
< 0.1%
129 1
< 0.1%
124 1
< 0.1%
118 1
< 0.1%
117 1
< 0.1%
116 2
< 0.1%
115 1
< 0.1%
111 2
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.9 MiB
f
238066 
w
157964 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters396030
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roww
2nd rowf
3rd rowf
4th rowf
5th rowf

Common Values

ValueCountFrequency (%)
f 238066
60.1%
w 157964
39.9%

Length

2024-10-07T02:55:29.173326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T02:55:29.213661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
f 238066
60.1%
w 157964
39.9%

Most occurring characters

ValueCountFrequency (%)
f 238066
60.1%
w 157964
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 396030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 238066
60.1%
w 157964
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 396030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 238066
60.1%
w 157964
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 396030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 238066
60.1%
w 157964
39.9%

application_type
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.3 MiB
INDIVIDUAL
395319 
JOINT
 
425
DIRECT_PAY
 
286

Length

Max length10
Median length10
Mean length9.9946342
Min length5

Characters and Unicode

Total characters3958175
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowINDIVIDUAL
4th rowINDIVIDUAL
5th rowINDIVIDUAL

Common Values

ValueCountFrequency (%)
INDIVIDUAL 395319
99.8%
JOINT 425
 
0.1%
DIRECT_PAY 286
 
0.1%

Length

2024-10-07T02:55:29.258357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T02:55:29.302142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
individual 395319
99.8%
joint 425
 
0.1%
direct_pay 286
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I 1186668
30.0%
D 790924
20.0%
N 395744
 
10.0%
A 395605
 
10.0%
V 395319
 
10.0%
U 395319
 
10.0%
L 395319
 
10.0%
T 711
 
< 0.1%
J 425
 
< 0.1%
O 425
 
< 0.1%
Other values (6) 1716
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3958175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1186668
30.0%
D 790924
20.0%
N 395744
 
10.0%
A 395605
 
10.0%
V 395319
 
10.0%
U 395319
 
10.0%
L 395319
 
10.0%
T 711
 
< 0.1%
J 425
 
< 0.1%
O 425
 
< 0.1%
Other values (6) 1716
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3958175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1186668
30.0%
D 790924
20.0%
N 395744
 
10.0%
A 395605
 
10.0%
V 395319
 
10.0%
U 395319
 
10.0%
L 395319
 
10.0%
T 711
 
< 0.1%
J 425
 
< 0.1%
O 425
 
< 0.1%
Other values (6) 1716
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3958175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1186668
30.0%
D 790924
20.0%
N 395744
 
10.0%
A 395605
 
10.0%
V 395319
 
10.0%
U 395319
 
10.0%
L 395319
 
10.0%
T 711
 
< 0.1%
J 425
 
< 0.1%
O 425
 
< 0.1%
Other values (6) 1716
 
< 0.1%

mort_acc
Real number (ℝ)

MISSING  ZEROS 

Distinct33
Distinct (%)< 0.1%
Missing37795
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean1.8139908
Minimum0
Maximum34
Zeros139777
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:29.349316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum34
Range34
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1479305
Coefficient of variation (CV)1.1840911
Kurtosis4.4771757
Mean1.8139908
Median Absolute Deviation (MAD)1
Skewness1.6001324
Sum649835
Variance4.6136053
MonotonicityNot monotonic
2024-10-07T02:55:29.400768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 139777
35.3%
1 60416
15.3%
2 49948
 
12.6%
3 38049
 
9.6%
4 27887
 
7.0%
5 18194
 
4.6%
6 11069
 
2.8%
7 6052
 
1.5%
8 3121
 
0.8%
9 1656
 
0.4%
Other values (23) 2066
 
0.5%
(Missing) 37795
 
9.5%
ValueCountFrequency (%)
0 139777
35.3%
1 60416
15.3%
2 49948
 
12.6%
3 38049
 
9.6%
4 27887
 
7.0%
5 18194
 
4.6%
6 11069
 
2.8%
7 6052
 
1.5%
8 3121
 
0.8%
9 1656
 
0.4%
ValueCountFrequency (%)
34 1
 
< 0.1%
32 2
 
< 0.1%
31 2
 
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%
27 3
 
< 0.1%
26 2
 
< 0.1%
25 4
 
< 0.1%
24 10
< 0.1%
23 2
 
< 0.1%

pub_rec_bankruptcies
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing535
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.12164756
Minimum0
Maximum8
Zeros350380
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-10-07T02:55:29.448249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35617428
Coefficient of variation (CV)2.9279197
Kurtosis18.10416
Mean0.12164756
Median Absolute Deviation (MAD)0
Skewness3.4234404
Sum48111
Variance0.12686012
MonotonicityNot monotonic
2024-10-07T02:55:29.494829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 350380
88.5%
1 42790
 
10.8%
2 1847
 
0.5%
3 351
 
0.1%
4 82
 
< 0.1%
5 32
 
< 0.1%
6 7
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
(Missing) 535
 
0.1%
ValueCountFrequency (%)
0 350380
88.5%
1 42790
 
10.8%
2 1847
 
0.5%
3 351
 
0.1%
4 82
 
< 0.1%
5 32
 
< 0.1%
6 7
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 4
 
< 0.1%
6 7
 
< 0.1%
5 32
 
< 0.1%
4 82
 
< 0.1%
3 351
 
0.1%
2 1847
 
0.5%
1 42790
 
10.8%
0 350380
88.5%
Distinct393700
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size35.4 MiB
2024-10-07T02:55:29.682722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length69
Median length60
Mean length44.713951
Min length20

Characters and Unicode

Total characters17708066
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique391984 ?
Unique (%)99.0%

Sample

1st row0174 Michelle Gateway Mendozaberg, OK 22690
2nd row1076 Carney Fort Apt. 347 Loganmouth, SD 05113
3rd row87025 Mark Dale Apt. 269 New Sabrina, WV 05113
4th row823 Reid Ford Delacruzside, MA 00813
5th row679 Luna Roads Greggshire, VA 11650
ValueCountFrequency (%)
suite 88417
 
3.0%
apt 88400
 
3.0%
70466 56986
 
2.0%
30723 56548
 
1.9%
22690 56527
 
1.9%
48052 55920
 
1.9%
00813 45826
 
1.6%
29597 45472
 
1.6%
05113 45403
 
1.6%
box 28349
 
1.0%
Other values (108604) 2352838
80.6%
2024-10-07T02:55:29.942346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2128626
 
12.0%
e 911545
 
5.1%
a 735427
 
4.2%
t 702787
 
4.0%
r 656748
 
3.7%
0 624825
 
3.5%
i 580043
 
3.3%
o 579480
 
3.3%
n 551350
 
3.1%
2 487525
 
2.8%
Other values (57) 9749710
55.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17708066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2128626
 
12.0%
e 911545
 
5.1%
a 735427
 
4.2%
t 702787
 
4.0%
r 656748
 
3.7%
0 624825
 
3.5%
i 580043
 
3.3%
o 579480
 
3.3%
n 551350
 
3.1%
2 487525
 
2.8%
Other values (57) 9749710
55.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17708066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2128626
 
12.0%
e 911545
 
5.1%
a 735427
 
4.2%
t 702787
 
4.0%
r 656748
 
3.7%
0 624825
 
3.5%
i 580043
 
3.3%
o 579480
 
3.3%
n 551350
 
3.1%
2 487525
 
2.8%
Other values (57) 9749710
55.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17708066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2128626
 
12.0%
e 911545
 
5.1%
a 735427
 
4.2%
t 702787
 
4.0%
r 656748
 
3.7%
0 624825
 
3.5%
i 580043
 
3.3%
o 579480
 
3.3%
n 551350
 
3.1%
2 487525
 
2.8%
Other values (57) 9749710
55.1%

Interactions

2024-10-07T02:55:23.329581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:14.647807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.508546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.283776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.220433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.913798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.657407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.389488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.246328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.013071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.734981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.458874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.391939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:14.743588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.575294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.361867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.281308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.977634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.732733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.448983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.312178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.079596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.801044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.521813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.452530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:14.811882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.636031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.420204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.338893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.032922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.790438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.500621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.369870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.141418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.860099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.579709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.515360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:14.882565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.706258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.485508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.398026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.093207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.852739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.571300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.434028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.202657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.925244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.641017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.574106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:14.944024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.770204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.550656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.450094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.148287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.908321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.634984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.498192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.258817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.981303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.706389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.632925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.027347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.827745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.619118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.507561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.202837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.968459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.691556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.568600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.314592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.042814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.773654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.691236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.097377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.897099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.689768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.564595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.261620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.024850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.742630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.630375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.374696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.100080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.828911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.753364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.168581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.953964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.907301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.623009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.320238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.083098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.800570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.688183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.431429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.156440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.885593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.821121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.236319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.015027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.974998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.680305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.387238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.148078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.860718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.758144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.490947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.218917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.947318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.882764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.304142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.084645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.035998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.733846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.452160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.207209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.917700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.814425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.553967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.276001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.009826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.947368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.369399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.148372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.100882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.792679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.519835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.268858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.125023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.877688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.610621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.334688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.210847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:24.008814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:15.436121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:16.226138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.162022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:17.852849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:18.587582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:19.329452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.182101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:20.941658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:21.672785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:22.396567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T02:55:23.269037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-07T02:55:30.000080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
annual_incapplication_typedtiemp_lengthgradehome_ownershipinitial_list_statusinstallmentint_rateloan_amntloan_statusmort_accopen_accpub_recpub_rec_bankruptciespurposerevol_balrevol_utilsub_gradetermtotal_accverification_status
annual_inc1.0000.000-0.2030.0000.0010.0000.0030.470-0.0970.4890.0030.3790.240-0.046-0.0720.0020.3930.0600.0000.0000.3340.005
application_type0.0001.0000.0480.0030.0300.0110.0270.0150.0490.0240.0120.0070.0120.0000.0020.0030.0000.0000.0360.0150.0090.014
dti-0.2030.0481.0001.0000.0000.0000.0000.0560.1720.0530.002-0.0480.323-0.042-0.0330.0000.2500.1850.0000.0000.2370.000
emp_length0.0000.0031.0001.0000.0050.0950.0430.0330.0070.0360.0170.0560.0180.0040.0160.0270.0050.0110.0050.0620.0460.053
grade0.0010.0300.0000.0051.0000.0400.0560.1040.7210.0970.2580.0290.0160.0040.0380.0890.0020.0981.0000.4680.0280.160
home_ownership0.0000.0110.0000.0950.0401.0000.0480.0710.0440.0840.0680.1380.0600.0000.0050.0860.0150.0140.0470.1000.1040.046
initial_list_status0.0030.0270.0000.0430.0560.0481.0000.0580.0660.0820.0090.0170.0650.0000.0410.0820.0120.0290.0660.1050.0650.089
installment0.4700.0150.0560.0330.1040.0710.0581.0000.1370.9680.0570.2020.208-0.093-0.1030.0970.4600.1320.0930.2570.2170.217
int_rate-0.0970.0490.1720.0070.7210.0440.0660.1371.0000.1310.246-0.1030.0040.0720.0610.0710.0060.3040.7210.441-0.0510.167
loan_amnt0.4890.0240.0530.0360.0970.0840.0820.9680.1311.0000.0650.2310.215-0.100-0.1090.1000.4700.1050.0840.4110.2370.234
loan_status0.0030.0120.0020.0170.2580.0680.0090.0570.2460.0651.0000.0550.0280.0060.0100.0590.0080.0390.2640.1730.0200.086
mort_acc0.3790.007-0.0480.0560.0290.1380.0170.202-0.1030.2310.0551.0000.1420.0320.0400.0210.2390.0080.0250.0690.4050.049
open_acc0.2400.0120.3230.0180.0160.0600.0650.2080.0040.2150.0280.1421.000-0.019-0.0250.0360.364-0.1390.0160.0760.6720.044
pub_rec-0.0460.000-0.0420.0040.0040.0000.000-0.0930.072-0.1000.0060.032-0.0191.0000.8620.007-0.209-0.0950.0060.0010.0330.004
pub_rec_bankruptcies-0.0720.002-0.0330.0160.0380.0050.041-0.1030.061-0.1090.0100.040-0.0250.8621.0000.014-0.205-0.0910.0360.0200.0420.032
purpose0.0020.0030.0000.0270.0890.0860.0820.0970.0710.1000.0590.0210.0360.0070.0141.0000.0040.0260.0630.0880.0340.062
revol_bal0.3930.0000.2500.0050.0020.0150.0120.4600.0060.4700.0080.2390.364-0.209-0.2050.0041.0000.4200.0000.0060.2940.014
revol_util0.0600.0000.1850.0110.0980.0140.0290.1320.3040.1050.0390.008-0.139-0.095-0.0910.0260.4201.0000.1010.022-0.1050.039
sub_grade0.0000.0360.0000.0051.0000.0470.0660.0930.7210.0840.2640.0250.0160.0060.0360.0630.0000.1011.0000.4810.0250.169
term0.0000.0150.0000.0620.4680.1000.1050.2570.4410.4110.1730.0690.0760.0010.0200.0880.0060.0220.4811.0000.1010.218
total_acc0.3340.0090.2370.0460.0280.1040.0650.217-0.0510.2370.0200.4050.6720.0330.0420.0340.294-0.1050.0250.1011.0000.061
verification_status0.0050.0140.0000.0530.1600.0460.0890.2170.1670.2340.0860.0490.0440.0040.0320.0620.0140.0390.1690.2180.0611.000

Missing values

2024-10-07T02:55:24.170672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-07T02:55:24.649393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-07T02:55:25.438243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

loan_amnttermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspurposetitledtiearliest_cr_lineopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusapplication_typemort_accpub_rec_bankruptciesaddress
010000.036 months11.44329.48BB4Marketing10+ yearsRENT117000.0Not Verified2015-01-01Fully PaidvacationVacation26.241990-06-0116.00.036369.041.825.0wINDIVIDUAL0.00.00174 Michelle Gateway\r\nMendozaberg, OK 22690
18000.036 months11.99265.68BB5Credit analyst4 yearsMORTGAGE65000.0Not Verified2015-01-01Fully Paiddebt_consolidationDebt consolidation22.052004-07-0117.00.020131.053.327.0fINDIVIDUAL3.00.01076 Carney Fort Apt. 347\r\nLoganmouth, SD 05113
215600.036 months10.49506.97BB3Statistician< 1 yearRENT43057.0Source Verified2015-01-01Fully Paidcredit_cardCredit card refinancing12.792007-08-0113.00.011987.092.226.0fINDIVIDUAL0.00.087025 Mark Dale Apt. 269\r\nNew Sabrina, WV 05113
37200.036 months6.49220.65AA2Client Advocate6 yearsRENT54000.0Not Verified2014-11-01Fully Paidcredit_cardCredit card refinancing2.602006-09-016.00.05472.021.513.0fINDIVIDUAL0.00.0823 Reid Ford\r\nDelacruzside, MA 00813
424375.060 months17.27609.33CC5Destiny Management Inc.9 yearsMORTGAGE55000.0Verified2013-04-01Charged Offcredit_cardCredit Card Refinance33.951999-03-0113.00.024584.069.843.0fINDIVIDUAL1.00.0679 Luna Roads\r\nGreggshire, VA 11650
520000.036 months13.33677.07CC3HR Specialist10+ yearsMORTGAGE86788.0Verified2015-09-01Fully Paiddebt_consolidationDebt consolidation16.312005-01-018.00.025757.0100.623.0fINDIVIDUAL4.00.01726 Cooper Passage Suite 129\r\nNorth Deniseberg, DE 30723
618000.036 months5.32542.07AA1Software Development Engineer2 yearsMORTGAGE125000.0Source Verified2015-09-01Fully Paidhome_improvementHome improvement1.362005-08-018.00.04178.04.925.0fINDIVIDUAL3.00.01008 Erika Vista Suite 748\r\nEast Stephanie, TX 22690
713000.036 months11.14426.47BB2Office Depot10+ yearsRENT46000.0Not Verified2012-09-01Fully Paidcredit_cardNo More Credit Cards26.871994-09-0111.00.013425.064.515.0fINDIVIDUAL0.00.0USCGC Nunez\r\nFPO AE 30723
818900.060 months10.99410.84BB3Application Architect10+ yearsRENT103000.0Verified2014-10-01Fully Paiddebt_consolidationDebt consolidation12.521994-06-0113.00.018637.032.940.0wINDIVIDUAL3.00.0USCGC Tran\r\nFPO AP 22690
926300.036 months16.29928.40CC5Regado Biosciences3 yearsMORTGAGE115000.0Verified2012-04-01Fully Paiddebt_consolidationDebt Consolidation23.691997-12-0113.00.022171.082.437.0fINDIVIDUAL1.00.03390 Luis Rue\r\nMauricestad, VA 00813
loan_amnttermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspurposetitledtiearliest_cr_lineopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusapplication_typemort_accpub_rec_bankruptciesaddress
39602010000.036 months9.76321.55BB3Retirement Counselor10+ yearsRENT40000.0Not Verified2015-12-01Fully Paiddebt_consolidationDebt consolidation23.401988-01-019.00.08819.057.318.0wINDIVIDUAL1.00.0914 Alexander Mountains Apt. 604\r\nEast Marco, VT 70466
3960213200.036 months5.4296.52AA1St Francis Medical Center10+ yearsRENT33000.0Not Verified2011-02-01Fully Paiddebt_consolidation2011 Insurance and Debt Consolidation21.451996-11-0118.00.03985.07.650.0fINDIVIDUALNaN0.0309 John Mission\r\nWest Marc, NY 00813
39602212000.036 months12.29400.24CC1Data Center Specialist II1 yearRENT52100.0Source Verified2015-10-01Fully Paiddebt_consolidationDebt consolidation17.282004-10-016.00.09580.066.118.0wINDIVIDUAL0.00.0532 Johnson Drive Apt. 185\r\nAndersonside, NY 70466
39602322000.036 months18.92805.55DD4Operations Manager10+ yearsMORTGAGE138000.0Not Verified2014-04-01Fully Paiddebt_consolidationDebt consolidation24.431998-05-0118.00.022287.050.439.0fINDIVIDUAL4.00.00297 Flores Dale Suite 441\r\nTaylorland, MD 05113
3960246000.036 months13.11202.49BB4Michael's Arts & Crafts5 yearsRENT64000.0Not Verified2013-03-01Fully Paiddebt_consolidationCredit buster10.811991-11-017.00.011456.097.19.0wINDIVIDUAL0.00.0514 Cynthia Park Apt. 402\r\nWest Williamside, SC 05113
39602510000.060 months10.99217.38BB4licensed bankere2 yearsRENT40000.0Source Verified2015-10-01Fully Paiddebt_consolidationDebt consolidation15.632004-11-016.00.01990.034.323.0wINDIVIDUAL0.00.012951 Williams Crossing\r\nJohnnyville, DC 30723
39602621000.036 months12.29700.42CC1Agent5 yearsMORTGAGE110000.0Source Verified2015-02-01Fully Paiddebt_consolidationDebt consolidation21.452006-02-016.00.043263.095.78.0fINDIVIDUAL1.00.00114 Fowler Field Suite 028\r\nRachelborough, LA 05113
3960275000.036 months9.99161.32BB1City Carrier10+ yearsRENT56500.0Verified2013-10-01Fully Paiddebt_consolidationpay off credit cards17.561997-03-0115.00.032704.066.923.0fINDIVIDUAL0.00.0953 Matthew Points Suite 414\r\nReedfort, NY 70466
39602821000.060 months15.31503.02CC2Gracon Services, Inc10+ yearsMORTGAGE64000.0Verified2012-08-01Fully Paiddebt_consolidationLoanforpayoff15.881990-11-019.00.015704.053.820.0fINDIVIDUAL5.00.07843 Blake Freeway Apt. 229\r\nNew Michael, FL 29597
3960292000.036 months13.6167.98CC2Internal Revenue Service10+ yearsRENT42996.0Verified2010-06-01Fully Paiddebt_consolidationToxic Debt Payoff8.321998-09-013.00.04292.091.319.0fINDIVIDUALNaN0.0787 Michelle Causeway\r\nBriannaton, AR 48052